Agree that patient input shouldn't be necessary, but to replace it we will need to include other inputs besides CGM in a systematic way to get the optimal results. My company is working on how to use contextual info automatically collected by your devices to help (detected activity, measured calorie burn, geofencing, data from meal-ordering apis, etc.). This is especially true given that the CGM data themselves are lagged due to averaging and/or kalman filtering going on under the hood. This is a fundamental problem; Inreda uses two identical CGMs for noise reduction purposes just so they can get clean data with less of a lag.

None of the systems claiming you don't have to do anything in terms of meal announcement are _working_ in the sense of achieving euglycemic parity, which should be the goal. I can say with certainty that the cgm logs from people who don't announce meals on the Inreda device do not look like they are from non-diabetics: there are still often large post-prandial spikes. Inreda likely does better than any single hormone system, but the problem is not solved in any sense.

> Agree that patient input shouldn't be necessary, but to replace it we will need to include other inputs besides CGM in a systematic way to get the optimal results.

I'm not going as far as to claim Medtronic's approach (I believe the only one commercially available with so-called meal prediction based on historical CGM and offers full correction boluses) is the optimal one, just that it is an approach that is at least very good (~80% time in target) and while it still requires meal announcements it's just the first step of what they're trying to do. Clearly we can expect further iterations of these algorithms as the technology matures.

> Inreda uses two identical CGMs for noise reduction purposes just so they can get clean data with less of a lag.

Just giving an example that this is possible without external input or data, your statement was that you will need a-priori information which is not necessarily the case. Whether such a system is optimal is a different question.

I haven't seen the raw data and highly doubt enough of it even exists for anyone to make a claim whether or not such a system can be optimized to the point necessary.

> None of the systems claiming you don't have to do anything in terms of meal announcement are _working_ in the sense of achieving euglycemic parity, which should be the goal.

For clarity to any less knowledgeable readers while time spent in euglycemia is a very important outcome measure it cannot come at the expense of severe hypoglycemia or severe hyperglycemia/diabetic ketoacidosis (i.e. an algorithm that improves euglycemia to 95% but has a 2% severe hypo time is less acceptable than 80% euglycemia and 0.5% severe hypo.)

To my knowledge no system on the market/generally available right now is claiming to be completely input free. The closest to my knowledge is again the MiniMed 780G discussed in my first point which will assuredly be iterated on.

Also to be clear I'm not being dismissive of what your company is working on, it's a very interesting and novel approach. It may even be necessary to achieve the optimal product. I look forward to reading about your results when you publish them. I'm just presenting alternatives and a brief overview of what other approaches are for HN readers who are likely unfamiliar with the topic being discussed.

Really appreciate the pointed commentary on this! Happy to make further prognostications about the success of CGM-input-only APSs via email.

For the record, when I say "Euglycemic Parity" what I really mean is a sort of Turing test (not time in range), where a data-driven Endocrinologist is asked to tell the difference between CGM records from a non-diabetic, and CGM records from a diabetic equipped with some control system. Passing this test should be our long term goal IMO and we will probably have to bring many techniques to bear to eventually achieve it.

I think the "no meal announcement" features are really valuable for traditionally underserved demographics who, for whatever reason, can't "get good" at managing their disease.

The difference between how quickly food and insulin hit your bloodstream make it seem like there is no way to "algorithm your way out of" meal announcements. Food hits almost immediately, and with variable strength depending on macronutrients in it, and insulin takes ~15 minutes to start working, and peaks at 1 hour, with no concern about BG levels. Can you square these 2 for me and make it make sense?

I think what you're missing for this to make sense is what is the desired outcome. For type 1 diabetics there are three important ones:

1. Time in severe hypoglycemia - ideally 0%

2. Time in severe hyperglycemia/diabetic ketoacidosis - ideally 0%

3. Time in euglycemia (also called time in target) - clinical target is >70% and for reference the median healthy non-diabetic is in target ~90-95% of the time.

Closed loop systems are very good at #1 and #2 as it takes a while for levels to get to the severe state and insulin can be administered (or withheld) based on CGM.

When we talk about algorithming out of meal announcements it's whether historical patient-specific blood glucose levels and insulin administrations (i.e. a prediction of what you eat and when) combined with CGM can keep #3 acceptable, not necessarily optimal. Medtronic is using this approach and their newest model more or less eliminates the need for accurate carb-counting but they still require meal announcements. The hope/idea is that this can potentially be eliminated in further iterations.

Another important thing to keep in mind which is sometimes lost in these discussions is that we don't treat numbers we treat patients (i.e. what are the clinical outcomes). Generally speaking, we assume the closer to normal the better but we don't have actual data about how much an extra X% outside of target ranges matters in terms of clinical outcomes and complication rates. We only really started getting this data with CGM and complications in these mild states would require very large cohorts and long (10-20 year) follow-ups to detect differences as they're likely to also be mild.

So while you're absolutely correct regarding the limitations and that an algorithm cannot outperform accurate carb-counting and meal announcements the missing piece is that it may be sufficient. Particularly if said algorithms result in improved time-in-target for patients who aren't good at managing their diabetes and find meal announcements cumbersome.

First of all, clearly you have a ton of knowledge in this space, and I'm feeling very lucky I get to learn from your experience here. I have one more last question/challenge:

I totally get that we "treat the patient" -- that's sort of what I'm hinting at with the demographics. But, in my opinion this thing you said:

> we don't have actual data about how much an extra X% outside of target ranges matters in terms of clinical outcomes and complication rates. We only really started getting this data with CGM and complications in these mild states would require very large cohorts and long (10-20 year) follow-ups to detect differences as they're likely to also be mild.

... is not the same as saying "we know that 70% TIR is safe to live with no complications". I don't think any of the guidelines are that confident, because there's not enough evidence yet. Consider this study (discussed at [1]):

> Overall, increasing time spent with glucose levels in the target range of 70–180 mg/dL (3.9–9.9 mmol/L) was associated with decreasing risk for microvascular complications. For instance, 50.0% of the 10 individuals in the lowest category for TIR (<40%) had at least one microvascular complication, compared with just 27.3% of the 99 people in the highest category for TIR (≥70%).

> Moreover, El Malahi said that the 180 people with microvascular complications had significantly lower average TIR than the 324 individuals without, at 60.4% versus 63.9%.

This suggests to me that a difference of 4% of TIR can seriously affect long-term outcomes. And, by the way, even those with the highest TIR still had microvascular complications higher than the normal population. On top of that, we know that genetic and environmental factors may be at play.

Therefore, my monkey math is that 75%+ as a TIR goal may mean that even if the patient is an order of magnitude more vulnerable to complications, microvascular or otherwise, they have a much better shot.

And, unfortunately, the typical western diet with 3 meals a day and snacks, you have to be "good at managing diabetes" to get to 75%+ TIR. Thanks for reading my ramble.

[1] https://diabetes.medicinematters.com/easd-2020/glycemic-cont...